#!/usr/bin/env python3
"""
Integration script for advanced training interface
Shows how to add comprehensive parameter controls to the main Gradio app
"""

import gradio as gr
from advanced_training_ui import create_advanced_training_interface, start_advanced_training, start_simple_training


def create_enhanced_app():
    """Create the main app with advanced training controls integrated."""
    
    with gr.Blocks(title="Dressify - Enhanced Outfit Recommendation", fill_height=True) as app:
        gr.Markdown("## 🏆 Dressify – Advanced Outfit Recommendation System\n*Research-grade, self-contained outfit recommendation with comprehensive training controls*")
        
        with gr.Tabs():
            # Main recommendation tab
            with gr.Tab("🎨 Recommend"):
                gr.Markdown("### Upload wardrobe images and generate outfit recommendations")
                # ... your existing recommendation interface
                pass
            
            # Advanced training tab
            with gr.Tab("🔬 Advanced Training"):
                # Create the advanced training interface
                training_interface, components = create_advanced_training_interface()
                
                # Set up event handlers for the training interface
                components['start_btn'].click(
                    fn=start_simple_training,
                    inputs=[components['resnet_epochs'], components['vit_epochs']],
                    outputs=components['train_log']
                )
                
                components['start_advanced_btn'].click(
                    fn=start_advanced_training,
                    inputs=[
                        # ResNet parameters
                        components['resnet_epochs'], components['resnet_batch_size'], components['resnet_lr'],
                        components['resnet_optimizer'], components['resnet_weight_decay'], components['resnet_triplet_margin'],
                        components['resnet_embedding_dim'], components['resnet_backbone'], components['resnet_use_pretrained'],
                        components['resnet_dropout'], 
                        
                        # ViT parameters
                        components['vit_epochs'], components['vit_batch_size'], components['vit_lr'],
                        components['vit_optimizer'], components['vit_weight_decay'], components['vit_triplet_margin'],
                        components['vit_embedding_dim'], components['vit_num_layers'], components['vit_num_heads'],
                        components['vit_ff_multiplier'], components['vit_dropout'], 
                        
                        # Advanced parameters
                        components['use_mixed_precision'], components['channels_last'], components['gradient_clip'],
                        components['warmup_epochs'], components['scheduler_type'], components['early_stopping_patience'],
                        components['mining_strategy'], components['augmentation_level'], components['seed']
                    ],
                    outputs=components['train_log']
                )
            
            # Simple training tab
            with gr.Tab("🚀 Simple Training"):
                gr.Markdown("### Quick training with default parameters")
                epochs_res = gr.Slider(1, 50, value=10, step=1, label="ResNet epochs")
                epochs_vit = gr.Slider(1, 100, value=20, step=1, label="ViT epochs")
                train_log = gr.Textbox(label="Training Log", lines=10)
                start_btn = gr.Button("Start Training")
                start_btn.click(fn=start_simple_training, inputs=[epochs_res, epochs_vit], outputs=train_log)
            
            # Other tabs...
            with gr.Tab("📊 Embed (Debug)"):
                gr.Markdown("### Debug image embeddings")
                # ... your existing embed interface
                pass
            
            with gr.Tab("📥 Downloads"):
                gr.Markdown("### Download trained models and artifacts")
                # ... your existing downloads interface
                pass
            
            with gr.Tab("📈 Status"):
                gr.Markdown("### System status and monitoring")
                # ... your existing status interface
                pass
    
    return app


def create_minimal_integration():
    """Minimal integration example - just add the advanced training tab to existing app."""
    
    # This shows how to add just the advanced training interface to your existing app.py
    
    # 1. Import the advanced training functions
    from advanced_training_ui import create_advanced_training_interface, start_advanced_training
    
    # 2. In your existing app.py, add this tab:
    """
    with gr.Tab("🔬 Advanced Training"):
        # Create the advanced training interface
        training_interface, components = create_advanced_training_interface()
        
        # Set up event handlers
        components['start_advanced_btn'].click(
            fn=start_advanced_training,
            inputs=[
                components['resnet_epochs'], components['resnet_batch_size'], components['resnet_lr'],
                components['resnet_optimizer'], components['resnet_weight_decay'], components['resnet_triplet_margin'],
                components['resnet_embedding_dim'], components['resnet_backbone'], components['resnet_use_pretrained'],
                components['resnet_dropout'], components['vit_epochs'], components['vit_batch_size'], components['vit_lr'],
                components['vit_optimizer'], components['vit_weight_decay'], components['vit_triplet_margin'],
                components['vit_embedding_dim'], components['vit_num_layers'], components['vit_num_heads'],
                components['vit_ff_multiplier'], components['vit_dropout'], components['use_mixed_precision'],
                components['channels_last'], components['gradient_clip'], components['warmup_epochs'],
                components['scheduler_type'], components['early_stopping_patience'], components['mining_strategy'],
                components['augmentation_level'], components['seed']
            ],
            outputs=components['train_log']
        )
    """
    
    print("✅ Advanced training interface ready for integration!")
    print("📝 Copy the code above into your existing app.py")


def show_parameter_examples():
    """Show examples of different parameter combinations."""
    
    examples = {
        "Quick Experiment": {
            "resnet_epochs": 5,
            "vit_epochs": 10,
            "batch_size": 16,
            "learning_rate": 1e-3,
            "description": "Fast training for parameter testing"
        },
        "Balanced Training": {
            "resnet_epochs": 20,
            "vit_epochs": 30,
            "batch_size": 64,
            "learning_rate": 1e-3,
            "description": "Standard quality training (default)"
        },
        "High Quality": {
            "resnet_epochs": 50,
            "vit_epochs": 100,
            "batch_size": 32,
            "learning_rate": 5e-4,
            "description": "Production-quality models"
        },
        "Research Mode": {
            "resnet_backbone": "resnet101",
            "embedding_dim": 768,
            "transformer_layers": 8,
            "attention_heads": 12,
            "mining_strategy": "hardest",
            "description": "Maximum model capacity"
        }
    }
    
    print("🎯 Parameter Combination Examples:")
    print("=" * 50)
    
    for name, params in examples.items():
        print(f"\n📋 {name}:")
        for key, value in params.items():
            if key != "description":
                print(f"   {key}: {value}")
        print(f"   💡 {params['description']}")


if __name__ == "__main__":
    print("🚀 Dressify Advanced Training Integration")
    print("=" * 50)
    
    print("\n1️⃣ Create enhanced app with all features:")
    print("   enhanced_app = create_enhanced_app()")
    
    print("\n2️⃣ Minimal integration into existing app:")
    create_minimal_integration()
    
    print("\n3️⃣ Parameter combination examples:")
    show_parameter_examples()
    
    print("\n✅ Integration complete! Your app now has comprehensive training controls.")
    print("\n📚 See TRAINING_PARAMETERS.md for detailed parameter explanations.")
    print("🔧 Use the advanced training interface to experiment with different configurations.")